From 2c0618214721bdae85d9164cfc2f5901dec4e96d Mon Sep 17 00:00:00 2001 From: David Wells Date: Sat, 20 Feb 2016 17:15:23 -0500 Subject: [PATCH] Remove some trailing whitespace. --- examples/step-20/doc/intro.dox | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/step-20/doc/intro.dox b/examples/step-20/doc/intro.dox index bc1d59228f..63ca74a0f7 100644 --- a/examples/step-20/doc/intro.dox +++ b/examples/step-20/doc/intro.dox @@ -352,15 +352,15 @@ on the diagonal and none of the usual preconditioners (Jacobi, SSOR) will work as they require division by diagonal elements. For the matrix sizes we expect to run with this program, the by far simplest -approach would be to just use a direct solver (in particular, the +approach would be to just use a direct solver (in particular, the SparseDirectUMFPACK class that is bundled with deal.II). step-29 goes this route and shows that solving any linear system can be done in just -3 or 4 lines of code. +3 or 4 lines of code. But then, this is a tutorial: we teach how to do things. Consequently, in the following, we will introduce some techniques that can be used in cases like these. Namely, we will consider the linear system as not consisting of one -large matrix and vectors, but we will want to decompose matrices +large matrix and vectors, but we will want to decompose matrices into blocks that correspond to the individual operators that appear in the system. We note that the resulting solver is not optimal -- there are much better ways, for example those explained in the results section of step-22 or -- 2.39.5